Population denominators NBS has not yet published official projections However we have Census 2012 data for Regions and LGAs Specific age groups U1 U5 WRA We also have official inter censal ID: 474361
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Slide1
AHSPPR FY 2013/14 highlightsSlide2
Population denominators
NBS has not yet published official projections
However, we have Census 2012 data for:
Regions and LGAs
Specific age groups (U1, U5, WRA)
We also have official inter-
censal
growth rates for all regions (Census 2012, p2)
We therefore used these to provide “best estimate” denominators pending the publication of official projectionsSlide3
Health status indicatorsSlide4
Indicator
Baseline (2008)
Latest data (source)
Target (2015)
Life expectancy at birth (yrs)
F52 M 51F62 M60 F62 M59 Neonatal mortality rate (per 1,000 live births) 3226 (TDHS 2010)21.4 (UN 2012)19Infant mortality rate (per 1,000 live births) 5845 (Census 2012) 50U5 mortality rate (per 1,000 live births) 9481 (TDHS 2010)54 (UN 2012) 48
Health status indicators Slide5
5
The trend in the Maternal Mortality
per 100,000 Live BirthsSlide6
Indicator
Baseline (2008)
Latest data (source)
Target (2015)
% U5 severely underweight
3.70%TBD 2.00%% U5 severely stunted 38%42% (TDHS 2010)35% (NPS 2011)20%Total Fertility Rate 5.75.2 (Census 2012) TrendHealth status indicators Slide7
Health service indicatorsSlide8
Top five
outpatient (OPD) diagnoses trends 2011 to 2013 using
HMIS and
SPDs
<5 Years
5 and Above Health Management Information System (HMIS)<5 Years 5 and Above Sentinel panel Districts (SPDs)Slide9
Top
five
causes of admission (IPD diagnoses
); HMIS and SPDs 2011
to
2013<5 Years 5 and Above <5 Years 5 and Above Health Management Information System (HMIS)Sentinel panel Districts (SPDs)Slide10
Top FIVE causes of deaths for persons aged
under five and 5
years and above, HMIS (and SPD)
<5 Years
5 and Above
Health Management Information System (HMIS)Sentinel Panel Districts (SPDs)<5 Years 5 and Above Slide11
Conclusion
No
significant
change in the proportions for the top three OPD diagnosis in three consecutive years. SPD data suggest reduction in the proportion of diagnosis of malaria in both under fives and five and years and above
Malaria was consistently the leading cause of admission over the last three years, and by a great margin.
Proportion of malaria among U5 decreased in 2013 compared with 2012 and 2011 (HMIS). Malaria, pneumonia and anaemia accounted for two thirds of reported U5 deaths in 2013 while HIV/AIDS, Malaria and TB account for 45% of deaths among 5 years and aboveSlide12
Per capita OP attendances, 2011 - 13
Target = 1.0Slide13
Mwanza
0.70
Geita
3.6
Simiyu
0.29
Shinyanga 0.57
Tabora
0.45
Singida
0.76
Dodoma 0.42
Iringa
0.71
=
Morogoro
0.91
Manyara
0.27
DDSM 0.66
Pwani
0.86
Lindi
0.74
Mtwara
0.66
Ruvuma 0.43
Njombe
0.66
Mbeya
0.50
Rukwa
0.62
Katavi
0.74
Kigoma
1.54
Kilimanjaro 0.70
Arusha
0.54
Mara 0.76
Kagera
0.48
Tanga
0.90
DSM 0.69
Regional
Per Capita OP
attendances, all ages, 2013
National Average 0.65
0 – 0.39
0.4 – 0. 59
0.6 – 0.79
0.8 – 1.0
> 1.0
KeySlide14
DTP3, Measles and TT2
vaccination coverage, 2011-13Slide15
Mwanza
81%
Geita
68
Simiyu
107%
Shinyanga 96%
Tabora
87%
Singida
70%
Dodoma 59%
Iringa
77%
=
Morogoro
98%
Manyara
71%
DDSM 0.66
Pwani
80%
Lindi
49%
Mtwara
52%
Ruvuma 86%
Njombe
166%
Mbeya
97%
Rukwa
105%
Katavi
53
%
Kigoma
73%
Kilimanjaro 51%
Arusha
78%
Mara 108%
Kagera
103%
Tanga
86%
DSM 74%
Regional TT2 vaccination coverage, 2013
National Average 89%
40 – 59%
60 – 89%
90 – 100%
> 100%
Key
0 – 39%Slide16
ANC early booking, 2011-13
Note: 2011 < 16 weeks; 2012 and 2013 < 12 weeksSlide17
Mwanza
40%
Geita
34
Simiyu
29%
Shinyanga 17%
Tabora
23%
Singida
33%
Dodoma 11%
Iringa
74%
=
Morogoro
107%
Manyara
24%
DDSM 0.66
Pwani
16%
Lindi
15%
Mtwara
22%
Ruvuma 56%
Njombe
38%
Mbeya
49%
Rukwa
60%
Katavi
68%
Kigoma
45%
Kilimanjaro 22%
Arusha
23%
Mara 37%
Kagera
24%
Tanga
40%
DSM 13%
Regional ANC 1
st
visit before 12 weeks, 2013
National Average 35%
0 – 39%
40 – 49%
50 – 78%
80 – 100%
> 100%
KeySlide18
Health facility deliveries, 2011-13Slide19
Mwanza
75%
Geita
57%
Simiyu
46%
Shinyanga 66%
Tabora
71%
Singida
60%
Dodoma 59%
Iringa
74%
=
Morogoro
66%
Manyara
32%
DDSM 0.66
Pwani
85%
Lindi
59%
Mtwara
48%
Ruvuma 78%
Njombe
68%
Mbeya
68%
Rukwa
100%
Katavi
73%
Kigoma
57%
Kilimanjaro 55%
Arusha
57%
Mara 56%
Kagera
45%
Tanga
46%
DSM 55%
Regional facility deliveries, 2013
National Average 61%
0 – 39%
40 – 59%
6
0 – 79%
80 – 100%
> 100%
KeySlide20
Family planning coverage, 2011-13Slide21
Mwanza
31%
Geita
18%
Simiyu
21%
Shinyanga 32%
Tabora
21%
Singida
57%
Dodoma 82%
Iringa
4
4%
=
Morogoro
37%
Manyara
31%
DDSM 0.66
Pwani
69%
Lindi
71%
Mtwara
69%
Ruvuma 78%
Njombe
68%
Mbeya
41%
Rukwa
43%
Katavi
40%
Kigoma
48%
Kilimanjaro 54%
Arusha
40%
Mara 41%
Kagera
38%
Tanga
61%
DSM 38%
Regional FP coverage,
2013
National Average 43%
0 – 39%
40 – 59%
6
0 – 79%
80 – 100%
KeySlide22
ART coverage, 2011-13Slide23
HIV
prevalence.Slide24
TB and leprosy
i
ndicators Slide25
Health systems indicatorsSlide26
Per capita public spending, 2011/12 – 2013/14Slide27
Mwanza
1.8%
Geita
1.6%
Simiyu
1.5%
Shinyanga
2.4%
Tabora
13.2%
Singida
29.8%
Dodoma 12.6%
Iringa
9.3%
=
Morogoro
9.7%
Manyara
3.3%
DDSM 0.66
Pwani
15.8%
Lindi
4.7%
Mtwara
3%
Ruvuma 9.9%
Njombe
9.7%
Mbeya
26.4%
Rukwa
12.2%
Katavi
13.8%
Kigoma
8.1%
Kilimanjaro 20.1%
Arusha
5.2%
Mara 2.7%
Kagera
1.3%
Tanga
14.1%
DSM 0%
Regional CHF coverage, 2013
National Average 8.7%
0 – 19%
20 – 39%
40 – 79%
80 – 100%
> 100%
KeySlide28
Mwanza
7
Geita
3.1%
Simiyu
2.5%
Shinyanga
4,9%
Tabora
2.9%
Singida
5.5
Dodoma 6.9
Iringa
11.3
=
Morogoro
7.9
Manyara
7.3%
DDSM 0.66
Pwani
9
.6
Lindi
8.3%
Mtwara
6.5
Ruvuma 7.2%
Njombe
10.9%
Mbeya
10.1
Rukwa
4.7%
Katavi
2.5%
Kigoma
3.3%
Kilimanjaro 14.8
Arusha
8.6
Mara 6
Kagera
5.2
Tanga
6.7
DSM 13
Human Resource (AMO, MO, Nurses/Nurse Midwife Laboratory staff) Per 10,000 Population by Region 2013
National Average 7.4
0 – 4.9%
5.0 – 6.9%
7.0 - 9.9%
>10
KeySlide29
Percentage of facilities with continuous availability of Tracer medicines, Jan-June 2014Slide30
Mwanza
7.1
Geita
5.8
Simiyu
6.6
Shinyanga
6.7
Tabora
8.1
Singida
8.1
Dodoma 7.2
Iringa
8,1
Morogoro
8.5
Manyara
8.1
DDSM 0.66
Pwani
6.8
Lindi
8.1
Mtwara
7.2
Ruvuma 7.8
Njombe
8.4
Mbeya
8.1
Rukwa
8.5
Katavi
8.2
Kigoma
7.1
Kilimanjaro 7.8
Arusha
8
Mara 7.8
Kagera
8
Tanga
8.4
DSM 7.4
Mean number of tracers available January – June 2013
National Average 7.7 Slide31
Challenges
Unsatisfactory quality of HMIS
data
under-reporting and delayed reporting from health facilities
Insufficient capacity for data analysis/summarization at health facility level
Lack of reliable population denominators Duplication of data collection through use of parallel reporting systemsInadequate data dissemination and useSlide32
Way forward
Strengthening of supportive supervision and mentoring of regions and councils
Quarterly analysis of HMIS data and review by the
M&E TWG
to identify data problems/issues and find out solution
Implement data quality audit activitiesEstablish a way for regular communication with regions to feed back and discuss the identified data quality issuesHarmonization of reporting systems for all programmes to prevent duplications and improve qualityImplement activities that will improve data dissemination and useStrengthen capacity for data collection, compilation at HF level and use of DHIS database